{"id":4837,"date":"2020-04-19T19:15:30","date_gmt":"2020-04-19T19:15:30","guid":{"rendered":"https:\/\/www.testpreptraining.com\/tutorial\/?page_id=4837"},"modified":"2020-04-19T19:15:30","modified_gmt":"2020-04-19T19:15:30","slug":"loading-data-google-professional-data-engineer-gcp","status":"publish","type":"page","link":"https:\/\/www.testpreptraining.ai\/tutorial\/loading-data-google-professional-data-engineer-gcp\/","title":{"rendered":"Loading Data Google Professional Data Engineer GCP"},"content":{"rendered":"<ul>\n<li>load data from\n<ul>\n<li>From Cloud Storage<\/li>\n<li>From other Google services<\/li>\n<li>From a readable data source<\/li>\n<li>By inserting individual records using streaming inserts<\/li>\n<li>Using DML statements to perform bulk inserts<\/li>\n<li>Using a BigQuery I\/O transform in a Dataflow pipeline to write data to BigQuery<\/li>\n<\/ul>\n<\/li>\n<li>can load data into a new table or partition<\/li>\n<li>can also append data to an existing table or partition<\/li>\n<li>can overwrite a table or partition.<\/li>\n<li>method for ingesting data into BigQuery:\n<ul>\n<li>the BigQuery Jobs API<\/li>\n<li>streaming writes<\/li>\n<li>writing query results into a table<\/li>\n<li>loading CSV files from Cloud Storage<\/li>\n<li>using BigQuery as a Cloud Dataflow sink<\/li>\n<\/ul>\n<\/li>\n<li>The default source format for loading data is CSV.<\/li>\n<li>Also supports streaming inserts by BigQuery API and BigQuery buffers records before insertion.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Load data from Cloud Storage and readable data sources in the following formats:<\/p>\n<p>Cloud Storage:<\/p>\n<ul>\n<li>Avro<\/li>\n<li>CSV<\/li>\n<li>JSON (newline delimited only)<\/li>\n<li>ORC<\/li>\n<li>Parquet<\/li>\n<li>Datastore exports<\/li>\n<li>Firestore exports<\/li>\n<\/ul>\n<p>Readable data source (such as local machine):<\/p>\n<ul>\n<li>Avro<\/li>\n<li>CSV<\/li>\n<li>JSON (newline delimited only)<\/li>\n<li>ORC<\/li>\n<li>Parquet<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>Choosing a data ingestion format<\/strong><\/p>\n<ul>\n<li>can load data into variety of formats.<\/li>\n<li>Loaded data is converted into columnar format for Capacitor (BigQuery&#8217;s storage format).<\/li>\n<li>During loading select data ingestion format based on\n<ul>\n<li>data&#8217;s schema<\/li>\n<li>Embedded newlines<\/li>\n<li>External limitations<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><strong>Loading encoded data<\/strong><\/p>\n<ul>\n<li>BigQuery supports UTF-8 encoding<\/li>\n<li>for both nested or repeated and flat data.<\/li>\n<li>ISO-8859-1 encoding for flat data only for CSV files.<\/li>\n<li>By default, the BigQuery service expects all source data to be UTF-8 encoded.<\/li>\n<\/ul>\n<p><strong>Loading compressed and uncompressed data<\/strong><\/p>\n<ul>\n<li>The Avro binary format is the preferred for loading both compressed and uncompressed data.<\/li>\n<\/ul>\n<p><strong>Loading denormalized, nested, and repeated data<\/strong><\/p>\n<ul>\n<li>BigQuery performs best when data is denormalized.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>Schema auto-detection<\/strong><\/p>\n<ul>\n<li>available when you load data into BigQuery<\/li>\n<li>Also when you query an external data source.<\/li>\n<li>Steps\n<ul>\n<li>BigQuery starts inference process by selecting a random file in the data source and scanning up to 100 rows of data to use as sample.<\/li>\n<li>then examines each field and attempts to assign a data type to that field based on the values in the sample.<\/li>\n<\/ul>\n<\/li>\n<li>use schema auto-detection for JSON or CSV files.<\/li>\n<li>not available for Avro files, ORC files, Parquet files, Datastore exports, or Firestore exports<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>BigQuery Data Transfer Service<\/strong><\/p>\n<p>It automates loading data into BigQuery from these services:<\/p>\n<p>Google Software as a Service (SaaS) apps<\/p>\n<ul>\n<li>Campaign Manager<\/li>\n<li>Cloud Storage<\/li>\n<li>Google Ad Manager<\/li>\n<li>Google Ads<\/li>\n<li>Google Merchant Center (beta)<\/li>\n<li>Google Play<\/li>\n<li>Search Ads 360 (beta)<\/li>\n<li>YouTube Channel reports<\/li>\n<li>YouTube Content Owner reports<\/li>\n<\/ul>\n<p>External cloud storage providers<\/p>\n<ul>\n<li>Amazon S3<\/li>\n<\/ul>\n<p>Data warehouses<\/p>\n<ul>\n<li>Teradata<\/li>\n<li>Amazon Redshift<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>load data from From Cloud Storage From other Google services From a readable data source By inserting individual records using streaming inserts Using DML statements to perform bulk inserts Using a BigQuery I\/O transform in a Dataflow pipeline to write data to BigQuery can load data into a new table or partition can also append&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"categories":[617],"tags":[706,709,619,623,622,618,621],"class_list":["post-4837","page","type-page","status-publish","hentry","category-google-gcp","tag-bigquery","tag-bigquery-loading-data","tag-data-engineer","tag-gcp","tag-google-certification","tag-google-cloud","tag-professional-data-engineer"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Loading Data Google Professional Data Engineer GCP - Testprep Training Tutorials<\/title>\n<meta name=\"description\" content=\"Google Cloud Certified Professional Data Engineer Tutorial, dumps, brief notes on Loading Data\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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